What are bank’s requirements for next generation analytics?

Today’s consumers have high expectations − and for good reason. They have become accustomed to, and in today’s connected world, demand, anytime/anywhere access to the products, services, and information they desire and need, writes Ambreesh Khanna

These expectations have carried over to their banking relationships. As a result, banks that wish to compete effectively are being forced to keep pace with rapidly changing customer demands, and analytics is on the front-lines of this quest. Just as customer expectations are evolving, so too are analytics solutions. This begs the question: are banks prepared to adopt and benefit from next-generation analytics solutions?

Before we answer that question, let’s look at the evolution of analytics and how we ended up where we are today. Thomas H. Davenport, research director of the International Institute for Analytics and a pioneer of Analytics 3.0, has the following perspective on the evolution of analytics:

Analytics version 1: Born in the mid 1950s, this generation of analytics incorporates basic business intelligence and key performance indicators to assess past performance. Analytics 1.0 enabled managers to examine data from production processes and customer interactions to improve the performance of their institutions

Analytics version 2: Emphasised by the big data boom of the mid-2000s, analytics 2.0 explored the power of predictive analytics and pulled data from multiple sources outside of a company – not just data that was generated solely by the organisation’s own internal systems

Analytics version 3: The next generation of analytics combines the descriptive and predictive qualities of past generations and adds an increased focus on prescriptive analytics – the use of models to specify optimal behaviours and actions

The 3.0 generation marks the point where analytics will be embedded as a part of real time decision-making and becomes a bank’s core of intelligence that enables it to have a full view of the customer like never before.

Putting the Customer First

Analytics 3.0 gives financial institutions the ability to put the customer first (e.g. a ‘customer-in’ approach) versus a ‘product out’ approach to banking. Analytics 3.0 promises to optimise value for both the bank and its customers by providing real time insights into what customers are doing and what their needs may be moving forward, whether they be mobile deposit capabilities or enhanced chat functions via a banking application.

In the world of analytics 3.0, the possibilities are endless. Banks can create personal, tailored services based on the data they collect. For example, social media channels provide the opportunity to capture rich data, such as information on life events, which may drive financial purchases. The insights that can be gleaned from this data enable custom, tailored offerings and marketing that truly speak to the customers and their needs. Further, this next generation of analytics can offer banks the ability to analyse customer transactions in real time and map behaviour against past trends – enabling these institutions to effectively coach customers toward behaviours that can optimise their investments, credit standing, and relationship with their banks.

But, before financial institutions can fully reap the rewards of this next generation of analytics, they must ask themselves several questions:

Will our infrastructure withstand the performance power we will need? High performance, scalable infrastructure is key to delivering the insights and intelligence needed in a timely fashion.

What types of data can we handle, and is it the right data? In the age of needing a 360-degree customer view to optimise profitability, firms must be able to handle multiple data types, and therefore a data model that can accommodate both structured and unstructured data from any and all sources.

Can our analytical applications put real-time insight into the hands of our employees? If real-time insight is the ‘holy grail’ of analytics, firms must ensure they are providing users with meaningful data at the fingertips.

How well are we integrated with our enterprise resource planning; enterprise risk management; governance, risk, compliance; customer insight; and enterprise performance management environments? We know that silos of data don’t deliver meaningful, actionable insights. It’s critical for banks to ask themselves if they have a solid integration between their analytical apps and existing enterprise apps.

Financial institutions are developing and perfecting their analytics capabilities, and should keep an eye out for future requirements and technologies. Today, the ability to gather, manage, and analyze vast amounts of data from all corners of a firm’s organization means ensuring that critical components are in place. The end result for banks is an improved ability to capture and retain loyal customers.